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Subject offerings of new and developing areas of knowledge in industrial engineering intended to augment the existing curriculum. See Class Schedule or departmental course information for topics and prerequisites. Course Information: Approved for letter and S/U grading. May be repeated in the same or separate terms if topics vary.

Introduction to statistical inference with probabilistic graphical models and low-complexity inference algorithms. In particular, we will treat the following methods: message-passing algorithms, belief propagation, loopy-belief propagation, variational methods, Markov chain Monte Carlo methods, learning structure. Applications and examples will include: Gaussian models, linear dynamical systems and hidden Markov models (forward-backward algorithm, Kalman filtering, Viterbi algorithm), computer vision, and machine learning (clustering, classification).